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feature-engine / feature_engine

Feature engineering package with sklearn like functionality
https://feature-engine.trainindata.com/
BSD 3-Clause "New" or "Revised" License
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Add code examples in transformation module's docstrings #646

Closed datacubeR closed 1 year ago

datacubeR commented 1 year ago

Adding examples for:

datacubeR commented 1 year ago

Finally, in the yeojohson example, could we remove the scaler and the wrapper? just showcase the transformer in question.

Sure thing. I added Scaler in a pipeline because with no scaling the YeoJohnson transform tends to return giant values that seems wrong (but they are not). I looked into the sklearn docs and their transformer have an standardized = True flag to deal with this, so I thought it could be a good idea to make results comparable.

codecov[bot] commented 1 year ago

Codecov Report

Merging #646 (af3aab8) into main (c2f6152) will not change coverage. The diff coverage is n/a.

@@           Coverage Diff           @@
##             main     #646   +/-   ##
=======================================
  Coverage   97.91%   97.91%           
=======================================
  Files         100      100           
  Lines        3748     3748           
  Branches      726      726           
=======================================
  Hits         3670     3670           
  Misses         29       29           
  Partials       49       49
Impacted Files Coverage Δ
feature_engine/transformation/arcsin.py 100.00% <ø> (ø)
feature_engine/transformation/boxcox.py 100.00% <ø> (ø)
feature_engine/transformation/log.py 92.94% <ø> (ø)
feature_engine/transformation/power.py 100.00% <ø> (ø)
feature_engine/transformation/reciprocal.py 100.00% <ø> (ø)
feature_engine/transformation/yeojohnson.py 100.00% <ø> (ø)

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